Why shift towards Edge Computing with Intelligence-AIoT?
IoT +Artificial Intelligence — a subtle revolution in Industry 4.0.
Edge computing and IoT are out there for a long time and are providing solutions based on connecting billions of smart devices to the internet and among themselves to transfer, visualize and find insight from sensor data. Edge Computing enlights us that processing and storing data at the edge has its own benefit rather than sending data back and forth to the servers for decision and other high computing processes but due to the limitation of resources and power constraints, earlier the edge devices have to send data to the central servers for complex decisions.
With the advent of Artificial Intelligence, now there is a tremendous improvement in handling decision making at the edge or you can say the devices are made so powerful that they can make the complex decisions on the fly by applying the power of machine learning and deep learning models run on the edge device board. Only the decisions or results are saved or triggered as a notification to the network. Few are the benefits which incorporate the power of edge computing and intelligence:
- Latency: there’s no round-trip to a server.
- Privacy: no data needs to leave the device.
- Connectivity: an Internet connection isn’t required.
- Power consumption: network connections are power-hungry.
- Low cost or maintenance: low-cost hardware accelerator.
- Perform an action:- the device can act by analyzing data.
Evolution of Low budget Edge Hardware Accelerator
- Nvidia-Jetson series nano, TX2, Xavier, etc
- Intel -Intel Movidius Neural Compute Stick
- Google-Coral board, coral stick
- Raspberry Pie
- AI-enabled chip: Qualcomm chip
- Handheld devices: GPU powered Mobiles; android, ios
There is a race going on building an AI Enable board(GPU, TPU)whether for processing input data pipeline or likely to be making decisions realtime from the data itself.
IoT vs AIoT Explained
AI — Preprocessing Pipeline:
The difference and benefit in regular data gathering and AI-enabled data acquisition can be explained using an example. Let’s take an instance of an IoT based connected system in which a camera device or camera sensors, capture every frame of the camera sensor and sends it to the storage setup while AIoT provides intelligence or brain to the system which could be machine learning or deep learning model for object detection, recognition or classification, etc to capture only that frame which has some meaningful insight like desired object or anomalies in it. Similarly, the same data acquisition goes to other formats of data like speech, unstructured or structured data, etc
AI — Action Pipeline:
This is the crux of the AIoT, making the whole system pro-active for the action to be performed based on the data processed. For instance, let’s consider an AIoT system for fire-fighting and building maintenance, so in this scenario, the edge device or sensor installed in the buildings will be sending continuous data to the storage system which further process, analyze, and finally act upon getting the results using some artificial intelligence -machine learning or deep learning model. This prevents further damage to the building. Similar can be the case of machinery equipment, their maintenance cost can be reduced.
Applications
There are a countless number of the application which can be used by combining the IoT and AI technologies together and leveraging to harness their power in the modern era. For instance, speech recognition, disease prediction, autonomous driving car, robotics, video analytics, and surveillance, face recognition systems, etc
There are multiple industry verticals that are going to be benefitted in the near future or already using these technologies. The application of IoT with AI can be used in different domain of industries like:
- Agriculture
- Healthcare
- Retail
- Security & surveillance
- Robotics & automation
- Factories and industries
The fusion of AI and IoT will make systems pro-active and intelligent which thus will save millions of dollars for the companies. For instance, in industrial AIoT setup if the system can predict maintenance of equipment in realtime thus can be shut down or alert for action which prevents them from damaging. In Refineries, leakage of oil can be known. In mining, AI intelligence action can even save the lives of people which is precious.
AIoT Market Trend & Growth
The number of IoT connections is expected to grow in both numbers and revenue. This increase in IoT market revenue share will be distributed across different industry sectors like utilities, manufacturing, transport and logistics, automotive, healthcare and so on.
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